Topology optimization (TO) is a structural optimization technique that searches for the
proper material distribution inside a design space such that an objective function is maximized/
minimized. Rapid prototyping technologies such as additive manufacturing (AM)
have allowed results from TO to be manufacturable. However, despite advancements in
their ability to manufacture complex geometries, AM technologies still face certain constraints
such as printing features at overhangs (unsupported features oriented at a certain
angle from the axis normal to the build plate) and small feature sizes, amongst others. In
the field of design for additive manufacturing (DfAM), it is common to only restrict one
constraint to control the quality of the final parts. However, several studies have found
that the final quality of a feature is heavily affected by at least two coupled constraints:
the overhanging angle and the feature’s thickness. Modifying a structure’s layout while
restricting only one constraint can uselessly increase the weight of a structure. To tackle
this problem, the work done in this thesis considers the interplay between two geometrical
constraints. The proposed research reviews some of the essential manufacturing constraints
in topology optimization and emphasizes the need for coupling existing constraints. It first
develops experiments to obtain a qualitative and a quantitative relationship between the
design features’ surface qualities, orientation, and thickness. The relation between those
parameters is used to update the layout of topologically optimized structures. The layout is
changed by obtaining the medial axis of topologically optimized structures and then using
implicit functions to conditionally thickening it. Throughout the analysis, it was observed
that both the inclination and the thickness affect the surface quality. Furthermore, the
effect of the parameters is more pronounced for low thicknesses and higher overhanging
angles. The overhanging angle impacts the surface quality more than the thickness, which
can be seen through ANOVA
